{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EnsembleVoteClassifier: A majority voting classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a majority voting `EnsembleVoteClassifier` for classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.classifier import EnsembleVoteClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `EnsembleVoteClassifier` is a meta-classifier for combining similar or conceptually different machine learning classifiers for classification via majority or plurality voting. (For simplicity, we will refer to both majority and plurality voting as majority voting.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![](./EnsembleVoteClassifier_files/voting.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `EnsembleVoteClassifier` implements \"hard\" and \"soft\" voting. In hard voting, we predict the final class label as the class label that has been predicted most frequently by the classification models. In soft voting, we predict the class labels by averaging the class-probabilities (only recommended if the classifiers are well-calibrated).\n",
    "\n",
    "![](./EnsembleVoteClassifier_files/majority_voting.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "If you are interested in using the <code>EnsembleVoteClassifier</code>, please note that it is now also available through scikit learn (>0.17) as <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html\"><code>VotingClassifier</code></a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Majority Voting / Hard Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hard voting is the simplest case of majority voting. Here, we predict the class label $\\hat{y}$ via majority (plurality) voting of each classifier $C_j$:\n",
    "\n",
    "$$\\hat{y}=mode\\{C_1(\\mathbf{x}), C_2(\\mathbf{x}), ..., C_m(\\mathbf{x})\\}$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Assuming that we combine three classifiers that classify a training sample as follows:\n",
    "\n",
    "- classifier 1 -> class 0\n",
    "- classifier 2 -> class 0\n",
    "- classifier 3 -> class 1\n",
    "\n",
    "$$\\hat{y}=mode\\{0, 0, 1\\} = 0$$\n",
    "\n",
    "Via majority vote, we would classify the sample as \"class 0.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted Majority Vote"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the simple majority vote (hard voting) as described in the previous section, we can compute a weighted majority vote by associating a weight $w_j$ with classifier $C_j$:\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\sum^{m}_{j=1} w_j \\chi_A \\big(C_j(\\mathbf{x})=i\\big),$$\n",
    "\n",
    "where $\\chi_A$ is the characteristic function $[C_j(\\mathbf{x}) = i \\; \\in A]$, and $A$ is the set of unique class labels. \n",
    "\n",
    "Continuing with the example from the previous section\n",
    "\n",
    "- classifier 1 -> class 0\n",
    "- classifier 2 -> class 0\n",
    "- classifier 3 -> class 1\n",
    "\n",
    "assigning the weights \\{0.2, 0.2, 0.6\\} would yield a prediction $\\hat{y} = 1$:\n",
    "\n",
    "$$\\arg \\max_i [0.2 \\times i_0 + 0.2 \\times i_0 + 0.6 \\times i_1] = 1$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In soft voting, we predict the class labels based on the predicted probabilities $p$ for classifier -- this approach is only recommended if the classifiers are well-calibrated.\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\sum^{m}_{j=1} w_j p_{ij},$$\n",
    "\n",
    "where $w_j$ is the weight that can be assigned to the $j$th classifier.\n",
    "\n",
    "Assuming the example in the previous section was a binary classification task with class labels $i \\in \\{0, 1\\}$, our ensemble could make the following prediction:\n",
    "\n",
    "- $C_1(\\mathbf{x}) \\rightarrow [0.9, 0.1]$\n",
    "- $C_2(\\mathbf{x}) \\rightarrow [0.8, 0.2]$\n",
    "- $C_3(\\mathbf{x}) \\rightarrow [0.4, 0.6]$\n",
    "\n",
    "Using uniform weights, we compute the average probabilities:\n",
    "\n",
    "$$p(i_0 \\mid \\mathbf{x}) = \\frac{0.9 + 0.8 + 0.4}{3} = 0.7 \\\\\\\\\n",
    "p(i_1 \\mid \\mathbf{x}) = \\frac{0.1 + 0.2 + 0.6}{3} = 0.3$$\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\big[p(i_0 \\mid \\mathbf{x}), p(i_1 \\mid \\mathbf{x}) \\big] = 0$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, assigning the weights \\{0.1, 0.1, 0.8\\} would yield a prediction $\\hat{y} = 1$:\n",
    "    \n",
    "$$p(i_0 \\mid \\mathbf{x}) = {0.1 \\times 0.9 + 0.1 \\times 0.8 + 0.8 \\times  0.4} = 0.49 \\\\\\\\\n",
    "p(i_1 \\mid \\mathbf{x}) = {0.1 \\times 0.1 + 0.2 \\times 0.1 + 0.8 \\times 0.6} = 0.51$$\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\big[p(i_0 \\mid \\mathbf{x}), p(i_1 \\mid \\mathbf{x}) \\big] = 1$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [1] S. Raschka. [Python Machine Learning](https://github.com/rasbt/python-machine-learning-book). Packt Publishing Ltd., 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 -  Classifying Iris Flowers Using Different Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation:\n",
      "\n",
      "Accuracy: 0.95 (+/- 0.04) [Logistic Regression]\n",
      "Accuracy: 0.94 (+/- 0.04) [Random Forest]\n",
      "Accuracy: 0.91 (+/- 0.04) [Naive Bayes]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "print('5-fold cross validation:\\n')\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes']\n",
    "\n",
    "for clf, label in zip([clf1, clf2, clf3], labels):\n",
    "\n",
    "    scores = model_selection.cross_val_score(clf, X, y, \n",
    "                                              cv=5, \n",
    "                                              scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\"\n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95 (+/- 0.04) [Logistic Regression]\n",
      "Accuracy: 0.94 (+/- 0.04) [Random Forest]\n",
      "Accuracy: 0.91 (+/- 0.04) [Naive Bayes]\n",
      "Accuracy: 0.95 (+/- 0.04) [Ensemble]\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,1])\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "for clf, label in zip([clf1, clf2, clf3, eclf], labels):\n",
    "\n",
    "    scores = model_selection.cross_val_score(clf, X, y, \n",
    "                                              cv=5, \n",
    "                                              scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" \n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Decision Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import matplotlib.gridspec as gridspec\n",
    "import itertools\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "for clf, lab, grd in zip([clf1, clf2, clf3, eclf],\n",
    "                         labels,\n",
    "                         itertools.product([0, 1], repeat=2)):\n",
    "\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf)\n",
    "    plt.title(lab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')\n",
    "\n",
    "params = {'logisticregression__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200],}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid.fit(iris.data, iris.target)\n",
    "    \n",
    "cv_keys = ('mean_test_score', 'std_test_score', 'params')\n",
    "\n",
    "for r, _ in enumerate(grid.cv_results_['mean_test_score']):\n",
    "    print(\"%0.3f +/- %0.2f %r\"\n",
    "          % (grid.cv_results_[cv_keys[0]][r],\n",
    "             grid.cv_results_[cv_keys[1]][r] / 2.0,\n",
    "             grid.cv_results_[cv_keys[2]][r]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: If the `EnsembleClassifier` is initialized with multiple similar estimator objects, the estimator names are modified with consecutive integer indices, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf1, clf2], \n",
    "                              voting='soft')\n",
    "\n",
    "params = {'logisticregression-1__C': [1.0, 100.0],\n",
    "          'logisticregression-2__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200],}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid = grid.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "The `EnsembleVoteClass` also enables grid search over the `clfs` argument. However, due to the current implementation of `GridSearchCV` in scikit-learn, it is not possible to search over both, differenct classifiers and classifier parameters at the same time. For instance, while the following parameter dictionary works\n",
    "\n",
    "    params = {'randomforestclassifier__n_estimators': [1, 100],\n",
    "    'clfs': [(clf1, clf1, clf1), (clf2, clf3)]}\n",
    "    \n",
    "it will use the instance settings of `clf1`, `clf2`, and `clf3` and not overwrite it with the `'n_estimators'` settings from `'randomforestclassifier__n_estimators': [1, 100]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3 - Majority voting with classifiers trained on different feature subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature selection algorithms implemented in scikit-learn as well as the `SequentialFeatureSelector` implement a `transform` method that passes the reduced feature subset to the next item in a `Pipeline`.\n",
    "\n",
    "For example, the method\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X[:, self.k_feature_idx_]\n",
    "        \n",
    "returns the best feature columns, `k_feature_idx_`, given a dataset X."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we simply need to construct a `Pipeline` consisting of the feature selector and the classifier in order to select different feature subsets for different algorithms. During `fitting`, the optimal feature subsets are automatically determined via the `GridSearchCV` object, and by calling `predict`, the fitted feature selector in the pipeline only passes these columns along, which resulted in the best performance for the respective classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, :], iris.target\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "# Creating a feature-selection-classifier pipeline\n",
    "\n",
    "sfs1 = SequentialFeatureSelector(clf1, \n",
    "                                 k_features=4,\n",
    "                                 forward=True, \n",
    "                                 floating=False, \n",
    "                                 scoring='accuracy',\n",
    "                                 verbose=0,\n",
    "                                 cv=0)\n",
    "\n",
    "clf1_pipe = Pipeline([('sfs', sfs1),\n",
    "                      ('logreg', clf1)])\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3], \n",
    "                              voting='soft')\n",
    "\n",
    "\n",
    "params = {'pipeline__sfs__k_features': [1, 2, 3],\n",
    "          'pipeline__logreg__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200]}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid.fit(iris.data, iris.target)\n",
    "\n",
    "\n",
    "\n",
    "cv_keys = ('mean_test_score', 'std_test_score', 'params')\n",
    "\n",
    "for r, _ in enumerate(grid.cv_results_['mean_test_score']):\n",
    "    print(\"%0.3f +/- %0.2f %r\"\n",
    "          % (grid.cv_results_[cv_keys[0]][r],\n",
    "             grid.cv_results_[cv_keys[1]][r] / 2.0,\n",
    "             grid.cv_results_[cv_keys[2]][r]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best parameters determined via GridSearch are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pipeline__logreg__C': 1.0,\n",
       " 'pipeline__sfs__k_features': 2,\n",
       " 'randomforestclassifier__n_estimators': 200}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we assign these parameters to the ensemble voting classifier, fit the models on the complete training set, and perform a prediction on 3 samples from the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = eclf.set_params(**grid.best_params_)\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can select different columns \"manually\" using the `ColumnSelector` object. In this example, we select only the first (sepal length) and third (petal length) column for the logistic regression classifier (`clf1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.feature_selection import ColumnSelector\n",
    "\n",
    "\n",
    "col_sel = ColumnSelector(cols=[0, 2])\n",
    "\n",
    "clf1_pipe = Pipeline([('sel', col_sel),\n",
    "                      ('logreg', clf1)])\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3],\n",
    "                              voting='soft')\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, we can fit the `SequentialFeatureSelector` separately, outside the grid search hyperparameter optimization pipeline. Here, we determine the best features first, and then we construct a pipeline using these \"fixed,\" best features as seed for the `ColumnSelector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s finished\n",
      "Features: 1/2[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s finished\n",
      "Features: 2/2"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features (2, 3)\n"
     ]
    }
   ],
   "source": [
    "sfs1 = SequentialFeatureSelector(clf1, \n",
    "                                 k_features=2,\n",
    "                                 forward=True, \n",
    "                                 floating=False, \n",
    "                                 scoring='accuracy',\n",
    "                                 verbose=1,\n",
    "                                 cv=0)\n",
    "\n",
    "sfs1.fit(X, y)\n",
    "\n",
    "print('Best features', sfs1.k_feature_idx_)\n",
    "\n",
    "col_sel = ColumnSelector(cols=sfs1.k_feature_idx_)\n",
    "\n",
    "clf1_pipe = Pipeline([('sel', col_sel),\n",
    "                      ('logreg', clf1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3], \n",
    "                              voting='soft')\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5 - Using Pre-fitted Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that we previously fitted our classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "for clf in (clf1, clf2, clf3):\n",
    "    clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By setting `fit_base_estimators=False`, it will enforce `use_clones` to be False and the `EnsembleVoteClassifier` will not re-fit these classifers to save computational time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.96\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/mlxtend/classifier/ensemble_vote.py:166: UserWarning: fit_base_estimators=False enforces use_clones to be `False`\n",
      "  warnings.warn(\"fit_base_estimators=False \"\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "import copy\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,1], fit_base_estimators=False)\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "\n",
    "eclf.fit(X, y)\n",
    "\n",
    "print('accuracy:', np.mean(y == eclf.predict(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, please note that `fit_base_estimators=False` is incompatible to any form of cross-validation that is done in e.g., `model_selection.cross_val_score` or `model_selection.GridSearchCV`, etc., since it would require the classifiers to be refit to the training folds. Thus, only use `fit_base_estimators=False` if you want to make a prediction directly without cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 6 - Ensembles of Classifiers that Operate on Different Feature Subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If desired, the different classifiers can be fit to different subsets of features in the training dataset. The following example illustrates how this can be done on a technical level using scikit-learn pipelines and the `ColumnSelector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EnsembleVoteClassifier(clfs=[Pipeline(steps=[('columnselector',\n",
       "                                              ColumnSelector(cols=(0, 2))),\n",
       "                                             ('logisticregression',\n",
       "                                              LogisticRegression())]),\n",
       "                             Pipeline(steps=[('columnselector',\n",
       "                                              ColumnSelector(cols=(1, 2, 3))),\n",
       "                                             ('logisticregression',\n",
       "                                              LogisticRegression())])])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from mlxtend.feature_selection import ColumnSelector\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)),\n",
    "                      LogisticRegression())\n",
    "pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),\n",
    "                      LogisticRegression())\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[pipe1, pipe2])\n",
    "\n",
    "eclf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 7 - A Note about Scikit-Learn SVMs and Soft Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section provides some additional technical insights in how probabilities are used when `voting='soft'`. \n",
    "\n",
    "\n",
    "Note that scikit-learn estimates the probabilities for SVMs (more info here: https://scikit-learn.org/stable/modules/svm.html#scores-probabilities) in a way that these may not be consistent with the class labels that the SVM predicts. This is an extreme example, but let's say we have a dataset with 3 class labels, 0, 1, and 2. For a given training example, the SVM classifier may predict class 2. However, the class-membership probabilities may look as follows:\n",
    "\n",
    "- class 0: 99%\n",
    "- class 1: 0.5%\n",
    "- class 2: 0.5%\n",
    "\n",
    "A practical example of this scenario is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============\n",
      "Probas from SVM            : [0.00921708 0.49415165 0.49663127]\n",
      "Class from SVM             : 1\n",
      "Probas from SVM in Ensemble: [0.00921708 0.49415165 0.49663127]\n",
      "Class from SVM in Ensemble : 2\n",
      "============\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/mlxtend/classifier/ensemble_vote.py:166: UserWarning: fit_base_estimators=False enforces use_clones to be `False`\n",
      "  warnings.warn(\"fit_base_estimators=False \"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "clf2 = SVC(probability=True, random_state=4)\n",
    "clf2.fit(X, y)\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf2], voting='soft', fit_base_estimators=False)\n",
    "eclf.fit(X, y)\n",
    "\n",
    "for svm_class, e_class, svm_prob, e_prob, in zip(clf2.predict(X),\n",
    "                                                 eclf.predict(X),\n",
    "                                                 clf2.predict_proba(X),\n",
    "                                                 eclf.predict_proba(X)):\n",
    "    if svm_class != e_class:\n",
    "        print('============')\n",
    "        print('Probas from SVM            :', svm_prob)\n",
    "        print('Class from SVM             :', svm_class)\n",
    "        print('Probas from SVM in Ensemble:', e_prob)\n",
    "        print('Class from SVM in Ensemble :', e_class)\n",
    "        print('============')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 7 - A Note about Scikit-Learn SVMs and Soft Voting\n",
    "\n",
    "\n",
    "Based on the probabilities, we would expect the SVM to predict class 2, because it has the highest probability. Since the `EnsembleVoteClassifier` uses the `argmax` function internally if `voting='soft'`, it would indeed predict class 2 in this case even if the ensemble consists of only one SVM model.\n",
    "\n",
    "Note that in practice, this minor technical detail does not need to concern you, but it is useful to keep it in mind in case you are wondering about results from a 1-model SVM ensemble compared to that SVM alone -- this is not a bug."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 8 - Optimizing Ensemble Weights with Nelder-Mead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will see how we can use a heuristic search method like [Nelder-Mead](https://en.wikipedia.org/wiki/Nelder–Mead_method) for optimizing the ensemble weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we have the following example scenario where we fit 3 individual classifiers on different subsets of the training dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from mlxtend.data import mnist_data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "X, y = mnist_data()\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.5, shuffle=True, random_state=1\n",
    ")\n",
    "\n",
    "clf1 = GaussianNB()\n",
    "clf2 = LogisticRegression(random_state=123, solver='newton-cg')\n",
    "clf3 = DecisionTreeClassifier(random_state=123, max_depth=2)\n",
    "\n",
    "clf1.fit(X_train[500:1000], y_train[500:1000])\n",
    "clf2.fit(X_train[750:1250], y_train[750:1250])\n",
    "clf3.fit(X_train[1250:2000], y_train[1250:2000]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we construct the an ensemble classifier from these 3 classifiers where each classifier contributes equally with weight 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/mlxtend/classifier/ensemble_vote.py:166: UserWarning: fit_base_estimators=False enforces use_clones to be `False`\n",
      "  warnings.warn(\"fit_base_estimators=False \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8012"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = EnsembleVoteClassifier(\n",
    "    clfs=(clf1, clf2, clf3),\n",
    "    voting=\"soft\",  # the same would also work with \"hard\" voting\n",
    "    weights=(1, 1, 1),\n",
    "    use_clones=False,\n",
    "    fit_base_estimators=False,\n",
    ")\n",
    "eclf.fit(X_train, y_train)\n",
    "eclf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we reach 80% accuracy on the validation set. Can we do better? Maybe they indvidual classifiers shouldn't be contributing equally. Perhaps, we can use an optimization algorothm from `scipy.optimize` to find a better relative weighting of these individual classifiers.\n",
    "\n",
    "Let's set up an objective function that we want to minimize via SciPy's [`minimize`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "\n",
    "def function_to_minimize(weights, fitted_clfs):\n",
    "\n",
    "    w1, w2 = weights  # these are the new weights!\n",
    "\n",
    "    newclf = EnsembleVoteClassifier(\n",
    "        voting=\"soft\",\n",
    "        use_clones=False,\n",
    "        fit_base_estimators=False,\n",
    "        clfs=fitted_clfs,\n",
    "        weights=(w1, w2, 1.),  # use the new weights\n",
    "    )\n",
    "\n",
    "    newclf.fit(X_train, y_train)\n",
    "    score = newclf.score(X_val, y_val)\n",
    "\n",
    "    # change accuracy to error so that smaller is better\n",
    "    score_to_minimize = 1 - score\n",
    "\n",
    "    return score_to_minimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note a few things:\n",
    "\n",
    "1. We only optimize 2 out of the 3 classifier weigths. That's because the weighting is relative to each other, and it would be overkill (and too many degrees of freedom) to also optimize weight 3.\n",
    "\n",
    "2. We set `use_clones=False & fit_base_estimators=False` as before, this is to make sure that we use the prefit classifiers in the ensemble classifier.\n",
    "\n",
    "3. Instead of optimizing the accuracy, we optimize the the classification error, `score_to_minimize = 1 - score`. That's because we use the `minimize` function where lower means better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's choose some initial weight values and run the optimization. Via the `bounds` we specify the range (lower and upper value) for each weight so that the search doesn't go crazy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "init_weights = [1., 1.]\n",
    "\n",
    "results = minimize(\n",
    "    function_to_minimize,\n",
    "    init_weights,\n",
    "    args=((clf1, clf2, clf3),),\n",
    "    bounds=((0, 5), (0, 5)),\n",
    "    method=\"nelder-mead\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[0.575     , 1.40625   ],\n",
      "       [0.57500153, 1.40622215],\n",
      "       [0.57508965, 1.40617647]]), array([0.1324, 0.1324, 0.1324]))\n",
      "           fun: 0.13239999999999996\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 60\n",
      "           nit: 21\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([0.575  , 1.40625])\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the search was successful and returned the following weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.575   1.40625]\n"
     ]
    }
   ],
   "source": [
    "solution = results[\"x\"]\n",
    "print(solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use these new weights in our ensemble classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/lib/python3.9/site-packages/mlxtend/classifier/ensemble_vote.py:166: UserWarning: fit_base_estimators=False enforces use_clones to be `False`\n",
      "  warnings.warn(\"fit_base_estimators=False \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8676"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = EnsembleVoteClassifier(\n",
    "    clfs=(clf1, clf2, clf3),\n",
    "    voting=\"soft\",\n",
    "    weights=(solution[0], solution[1], 1),\n",
    "    use_clones=False,\n",
    "    fit_base_estimators=False,\n",
    ")\n",
    "\n",
    "eclf.fit(X_train, y_train)\n",
    "eclf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the results on the validation set (0.8676) improved compared to the original ones (0.8012). Yay!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## EnsembleVoteClassifier\n",
      "\n",
      "*EnsembleVoteClassifier(clfs, voting='hard', weights=None, verbose=0, use_clones=True, fit_base_estimators=True)*\n",
      "\n",
      "Soft Voting/Majority Rule classifier for scikit-learn estimators.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `clfs` : array-like, shape = [n_classifiers]\n",
      "\n",
      "    A list of classifiers.\n",
      "    Invoking the `fit` method on the `VotingClassifier` will fit clones\n",
      "    of those original classifiers\n",
      "    be stored in the class attribute\n",
      "    if `use_clones=True` (default) and\n",
      "    `fit_base_estimators=True` (default).\n",
      "\n",
      "- `voting` : str, {'hard', 'soft'} (default='hard')\n",
      "\n",
      "    If 'hard', uses predicted class labels for majority rule voting.\n",
      "    Else if 'soft', predicts the class label based on the argmax of\n",
      "    the sums of the predicted probalities, which is recommended for\n",
      "    an ensemble of well-calibrated classifiers.\n",
      "\n",
      "- `weights` : array-like, shape = [n_classifiers], optional (default=`None`)\n",
      "\n",
      "    Sequence of weights (`float` or `int`) to weight the occurances of\n",
      "    predicted class labels (`hard` voting) or class probabilities\n",
      "    before averaging (`soft` voting). Uses uniform weights if `None`.\n",
      "\n",
      "- `verbose` : int, optional (default=0)\n",
      "\n",
      "    Controls the verbosity of the building process.\n",
      "    - `verbose=0` (default): Prints nothing\n",
      "    - `verbose=1`: Prints the number & name of the clf being fitted\n",
      "    - `verbose=2`: Prints info about the parameters of the clf being fitted\n",
      "    - `verbose>2`: Changes `verbose` param of the underlying clf to\n",
      "    self.verbose - 2\n",
      "\n",
      "- `use_clones` : bool (default: True)\n",
      "\n",
      "    Clones the classifiers for stacking classification if True (default)\n",
      "    or else uses the original ones, which will be refitted on the dataset\n",
      "    upon calling the `fit` method. Hence, if use_clones=True, the original\n",
      "    input classifiers will remain unmodified upon using the\n",
      "    StackingClassifier's `fit` method.\n",
      "    Setting `use_clones=False` is\n",
      "    recommended if you are working with estimators that are supporting\n",
      "    the scikit-learn fit/predict API interface but are not compatible\n",
      "    to scikit-learn's `clone` function.\n",
      "\n",
      "- `fit_base_estimators` : bool (default: True)\n",
      "\n",
      "    Refits classifiers in `clfs` if True; uses references to the `clfs`,\n",
      "    otherwise (assumes that the classifiers were already fit).\n",
      "    Note: fit_base_estimators=False will enforce use_clones to be False,\n",
      "    and is incompatible to most scikit-learn wrappers!\n",
      "    For instance, if any form of cross-validation is performed\n",
      "    this would require the re-fitting classifiers to training folds, which\n",
      "    would raise a NotFitterError if fit_base_estimators=False.\n",
      "    (New in mlxtend v0.6.)\n",
      "\n",
      "**Attributes**\n",
      "\n",
      "- `classes_` : array-like, shape = [n_predictions]\n",
      "\n",
      "\n",
      "- `clf` : array-like, shape = [n_predictions]\n",
      "\n",
      "    The input classifiers; may be overwritten if `use_clones=False`\n",
      "\n",
      "- `clf_` : array-like, shape = [n_predictions]\n",
      "\n",
      "    Fitted input classifiers; clones if `use_clones=True`\n",
      "\n",
      "**Examples**\n",
      "\n",
      "    ```\n",
      "    >>> import numpy as np\n",
      "    >>> from sklearn.linear_model import LogisticRegression\n",
      "    >>> from sklearn.naive_bayes import GaussianNB\n",
      "    >>> from sklearn.ensemble import RandomForestClassifier\n",
      "    >>> from mlxtend.sklearn import EnsembleVoteClassifier\n",
      "    >>> clf1 = LogisticRegression(random_seed=1)\n",
      "    >>> clf2 = RandomForestClassifier(random_seed=1)\n",
      "    >>> clf3 = GaussianNB()\n",
      "    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
      "    >>> y = np.array([1, 1, 1, 2, 2, 2])\n",
      "    >>> eclf1 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],\n",
      "    ... voting='hard', verbose=1)\n",
      "    >>> eclf1 = eclf1.fit(X, y)\n",
      "    >>> print(eclf1.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>> eclf2 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')\n",
      "    >>> eclf2 = eclf2.fit(X, y)\n",
      "    >>> print(eclf2.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>> eclf3 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],\n",
      "    ...                          voting='soft', weights=[2,1,1])\n",
      "    >>> eclf3 = eclf3.fit(X, y)\n",
      "    >>> print(eclf3.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>>\n",
      "\n",
      "    For more usage examples, please see\n",
      "    https://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/\n",
      "```\n",
      "\n",
      "### Methods\n",
      "\n",
      "<hr>\n",
      "\n",
      "*fit(X, y, sample_weight=None)*\n",
      "\n",
      "Learn weight coefficients from training data for each classifier.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "\n",
      "- `y` : array-like, shape = [n_samples]\n",
      "\n",
      "    Target values.\n",
      "\n",
      "\n",
      "- `sample_weight` : array-like, shape = [n_samples], optional\n",
      "\n",
      "    Sample weights passed as sample_weights to each regressor\n",
      "    in the regressors list as well as the meta_regressor.\n",
      "    Raises error if some regressor does not support\n",
      "    sample_weight in the fit() method.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `self` : object\n",
      "\n",
      "\n",
      "<hr>\n",
      "\n",
      "*fit_transform(X, y=None, **fit_params)*\n",
      "\n",
      "Fit to data, then transform it.\n",
      "\n",
      "    Fits transformer to `X` and `y` with optional parameters `fit_params`\n",
      "    and returns a transformed version of `X`.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : array-like of shape (n_samples, n_features)\n",
      "\n",
      "    Input samples.\n",
      "\n",
      "\n",
      "- `y` :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None\n",
      "\n",
      "    Target values (None for unsupervised transformations).\n",
      "\n",
      "\n",
      "- `**fit_params` : dict\n",
      "\n",
      "    Additional fit parameters.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `X_new` : ndarray array of shape (n_samples, n_features_new)\n",
      "\n",
      "    Transformed array.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*get_params(deep=True)*\n",
      "\n",
      "Return estimator parameter names for GridSearch support.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*predict(X)*\n",
      "\n",
      "Predict class labels for X.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `maj` : array-like, shape = [n_samples]\n",
      "\n",
      "    Predicted class labels.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*predict_proba(X)*\n",
      "\n",
      "Predict class probabilities for X.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `avg` : array-like, shape = [n_samples, n_classes]\n",
      "\n",
      "    Weighted average probability for each class per sample.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*score(X, y, sample_weight=None)*\n",
      "\n",
      "Return the mean accuracy on the given test data and labels.\n",
      "\n",
      "    In multi-label classification, this is the subset accuracy\n",
      "    which is a harsh metric since you require for each sample that\n",
      "    each label set be correctly predicted.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : array-like of shape (n_samples, n_features)\n",
      "\n",
      "    Test samples.\n",
      "\n",
      "\n",
      "- `y` : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      "\n",
      "    True labels for `X`.\n",
      "\n",
      "\n",
      "- `sample_weight` : array-like of shape (n_samples,), default=None\n",
      "\n",
      "    Sample weights.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `score` : float\n",
      "\n",
      "    Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*set_params(**params)*\n",
      "\n",
      "Set the parameters of this estimator.\n",
      "\n",
      "    The method works on simple estimators as well as on nested objects\n",
      "    (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      "    parameters of the form ``<component>__<parameter>`` so that it's\n",
      "    possible to update each component of a nested object.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `**params` : dict\n",
      "\n",
      "    Estimator parameters.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `self` : estimator instance\n",
      "\n",
      "    Estimator instance.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*transform(X)*\n",
      "\n",
      "Return class labels or probabilities for X for each estimator.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `If `voting='soft'`` : array-like = [n_classifiers, n_samples, n_classes]\n",
      "\n",
      "    Class probabilties calculated by each classifier.\n",
      "\n",
      "- `If `voting='hard'`` : array-like = [n_classifiers, n_samples]\n",
      "\n",
      "    Class labels predicted by each classifier.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.classifier/EnsembleVoteClassifier.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
